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New tools for automated cryo-EM single-particle analysis in RELION-4.0
Author(s) -
Dari Kimanius,
Liyi Dong,
Grigory Sharov,
Takanori Nakane,
Sjors H. W. Scheres
Publication year - 2021
Publication title -
biochemical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 265
eISSN - 1470-8728
pISSN - 0264-6021
DOI - 10.1042/bcj20210708
Subject(s) - computer science , workflow , metadata , software , gradient descent , data mining , convolutional neural network , cryo electron microscopy , artificial neural network , metric (unit) , artificial intelligence , pattern recognition (psychology) , database , operating system , biochemistry , chemistry , operations management , economics
We describe new tools for the processing of electron cryo-microscopy (cryo-EM) images in the fourth major release of the RELION software. In particular, we introduce VDAM, a variable-metric gradient descent algorithm with adaptive moments estimation, for image refinement; a convolutional neural network for unsupervised selection of 2D classes; and a flexible framework for the design and execution of multiple jobs in pre-defined workflows. In addition, we present a stand-alone utility called MDCatch that links the execution of jobs within this framework with metadata gathering during microscope data acquisition. The new tools are aimed at providing fast and robust procedures for unsupervised cryo-EM structure determination, with potential applications for on-the-fly processing and the development of flexible, high-throughput structure determination pipelines. We illustrate their potential on 12 publicly available cryo-EM data sets.

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